{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 为什么要使用multiprocessing模块\n",
    "python提供了os.fork创建进程，提供了thread模块创建线程，这不是足够了吗?\n",
    "\n",
    "这是不够的，os.fork无法在windows实现，不具有可移植性；线程虽然可以，但由于python的GIL(Global Interpreter Lock，全局解释器锁）的存在，同一时刻python只能运行一个线程，没法将多线程分配到多核上运行。\n",
    "\n",
    "这就是引入了multiprocessing模块的原因，解决了windows移植到问题和如何利用多核的问题。\n",
    "\n",
    "如何使用multiprocessing模块呢?就3步。\n",
    "\n",
    "①定义自己的任务，\n",
    "\n",
    "    #流水灯任务定义\n",
    "    def waterlight(task_name, delay_ts):\n",
    "        print(task_name + \"任务启动\")\n",
    "        try:\n",
    "            while True:\n",
    "                for i in range(0,8):\n",
    "                    SAKS.ledrow.on_for_index(i)\n",
    "                    time.sleep(delay_ts)\n",
    "                    SAKS.ledrow.off_for_index(i)\n",
    "        except KeyboardInterrupt:\n",
    "            print(task_name + \"任务被终止\")\n",
    "\n",
    "②将任务交给multiprocess模块，\n",
    "\n",
    "    #创建led闪烁任务\n",
    "    led_flash = Process(target=waterlight, args=(\"流水灯\", WATERLIGHT_DELAY))\n",
    "\n",
    "③启动该任务。\n",
    "\n",
    "    # 启动任务\n",
    "    led_flash.start()\n",
    "    \n",
    "看吧，就3步，是不是很简单。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 进程的状态\n",
    "从大的方面来说，进程在运行过程中有3种状态：就绪态、运行态、阻塞态。\n",
    "\n",
    "(1)就绪态。\n",
    "\n",
    "就绪态：进程具备运行条件了，可以被cpu执行了；但还在就绪队列中排着队。\n",
    "\n",
    "例如，我们led灯例程中有两个地方是就就绪态的。\n",
    "\n",
    "①进程名.start()执行后，进程进入就绪态了。\n",
    "\n",
    "②time.sleep()之后，点亮灯(或熄灭灯)之前，进程进入就绪态了。\n",
    "\n",
    "(2)运行态。\n",
    "\n",
    "运行态：进程就是在cpu上运行了。\n",
    "\n",
    "例如，执行把运行灯操作语句，把灯点亮(或者熄灭)时，这是就是运行态。\n",
    "\n",
    "(3)阻塞态。\n",
    "\n",
    "阻塞态：进程在等待某个事件发生的过程中，就是阻塞态；此时，一直在阻塞队列中等待着。\n",
    "\n",
    "例如，执行time.sleep语句，进行延时时。\n",
    "\n",
    "多任务好吧，你把做的事情告送操作系统，操作系统帮你进行管理、调度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 512
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进程状态图\n",
    "from IPython.display import Image\n",
    "Image(filename = \"./picture/processing_status.png\", width=512, height=468)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对比下，multiprocessing模块和thread模块的效率 \n",
    "下属代码及结论，摘抄自”莫烦python“，\n",
    "链接：https://morvanzhou.github.io/tutorials/python-basic/multiprocessing/4-comparison/\n",
    "\n",
    "结论：发现多核/多进程最快，说明在同时间运行了多个任务。 而多线程的运行时间居然比什么都不做的程序还要慢一点，说明多线程还是有一定的短板的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal: 499999666667166666000000\n",
      "normal time: 7.46769118309021\n",
      " \n",
      "multithread: 499999666667166666000000\n",
      "multithread time: 8.244486808776855\n",
      " \n",
      "multicore: 499999666667166666000000\n",
      "multicore time: 3.981152057647705\n",
      " \n"
     ]
    }
   ],
   "source": [
    "\n",
    "#要完成数据处理工作\n",
    "def job(q):\n",
    "    res = 0\n",
    "    for i in range(1000000):\n",
    "        res += i + i**2 + i**3\n",
    "    q.put(res) # queue\n",
    "\n",
    "#通常采用的１个进程进程处理    \n",
    "def normal():\n",
    "    res = 0\n",
    "    for _ in range(2):\n",
    "        for i in range(1000000):\n",
    "            res += i + i**2 + i**3\n",
    "    print('normal:', res)\n",
    "\n",
    "#多线程方式，将数据分布在两个线程中运行\n",
    "import threading as td\n",
    "\n",
    "def multithread():\n",
    "    q = mp.Queue() # thread可放入process同样的queue中\n",
    "    t1 = td.Thread(target=job, args=(q,))\n",
    "    t2 = td.Thread(target=job, args=(q,))\n",
    "    t1.start()\n",
    "    t2.start()\n",
    "    t1.join()\n",
    "    t2.join()\n",
    "    res1 = q.get()\n",
    "    res2 = q.get()\n",
    "    print('multithread:', res1 + res2)\n",
    "    \n",
    "\n",
    "    \n",
    "#multiprocessing多进程方式，将数据分布在两个进程中运行    \n",
    "import multiprocessing as mp\n",
    "\n",
    "def multicore():\n",
    "    q = mp.Queue()\n",
    "    p1 = mp.Process(target=job, args=(q,))\n",
    "    p2 = mp.Process(target=job, args=(q,))\n",
    "    p1.start()\n",
    "    p2.start()\n",
    "    p1.join()\n",
    "    p2.join()\n",
    "    res1 = q.get()\n",
    "    res2 = q.get()\n",
    "    print('multicore:',res1 + res2)\n",
    "    \n",
    "#依次调用普通处理、多线程处理、多进程处理\n",
    "import time\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    #普通模式处理，并计算计算时间\n",
    "    st = time.time()\n",
    "    normal()\n",
    "    st1 = time.time()\n",
    "    print('normal time:', st1 - st)\n",
    "    print(' ')\n",
    "    \n",
    "    #多线程模式处理，并计算计算时间\n",
    "    multithread()\n",
    "    st2 = time.time()\n",
    "    print('multithread time:', st2 - st1)\n",
    "    print(' ')\n",
    "    \n",
    "    #多进程模式处理，并计算计算时间\n",
    "    multicore()\n",
    "    print('multicore time:', time.time() - st2)\n",
    "    print(' ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package multiprocessing:\n",
      "\n",
      "NAME\n",
      "    multiprocessing\n",
      "\n",
      "MODULE REFERENCE\n",
      "    https://docs.python.org/3.7/library/multiprocessing\n",
      "    \n",
      "    The following documentation is automatically generated from the Python\n",
      "    source files.  It may be incomplete, incorrect or include features that\n",
      "    are considered implementation detail and may vary between Python\n",
      "    implementations.  When in doubt, consult the module reference at the\n",
      "    location listed above.\n",
      "\n",
      "DESCRIPTION\n",
      "    # Package analogous to 'threading.py' but using processes\n",
      "    #\n",
      "    # multiprocessing/__init__.py\n",
      "    #\n",
      "    # This package is intended to duplicate the functionality (and much of\n",
      "    # the API) of threading.py but uses processes instead of threads.  A\n",
      "    # subpackage 'multiprocessing.dummy' has the same API but is a simple\n",
      "    # wrapper for 'threading'.\n",
      "    #\n",
      "    # Copyright (c) 2006-2008, R Oudkerk\n",
      "    # Licensed to PSF under a Contributor Agreement.\n",
      "    #\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    connection\n",
      "    context\n",
      "    dummy (package)\n",
      "    forkserver\n",
      "    heap\n",
      "    managers\n",
      "    pool\n",
      "    popen_fork\n",
      "    popen_forkserver\n",
      "    popen_spawn_posix\n",
      "    popen_spawn_win32\n",
      "    process\n",
      "    queues\n",
      "    reduction\n",
      "    resource_sharer\n",
      "    semaphore_tracker\n",
      "    sharedctypes\n",
      "    spawn\n",
      "    synchronize\n",
      "    util\n",
      "\n",
      "SUBMODULES\n",
      "    reducer\n",
      "\n",
      "CLASSES\n",
      "    builtins.Exception(builtins.BaseException)\n",
      "        multiprocessing.context.ProcessError\n",
      "            multiprocessing.context.AuthenticationError\n",
      "            multiprocessing.context.BufferTooShort\n",
      "            multiprocessing.context.TimeoutError\n",
      "    multiprocessing.process.BaseProcess(builtins.object)\n",
      "        multiprocessing.context.Process\n",
      "    \n",
      "    class AuthenticationError(ProcessError)\n",
      "     |  Common base class for all non-exit exceptions.\n",
      "     |  \n",
      "     |  Method resolution order:\n",
      "     |      AuthenticationError\n",
      "     |      ProcessError\n",
      "     |      builtins.Exception\n",
      "     |      builtins.BaseException\n",
      "     |      builtins.object\n",
      "     |  \n",
      "     |  Data descriptors inherited from ProcessError:\n",
      "     |  \n",
      "     |  __weakref__\n",
      "     |      list of weak references to the object (if defined)\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __init__(self, /, *args, **kwargs)\n",
      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Static methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __new__(*args, **kwargs) from builtins.type\n",
      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __delattr__(self, name, /)\n",
      "     |      Implement delattr(self, name).\n",
      "     |  \n",
      "     |  __getattribute__(self, name, /)\n",
      "     |      Return getattr(self, name).\n",
      "     |  \n",
      "     |  __reduce__(...)\n",
      "     |      Helper for pickle.\n",
      "     |  \n",
      "     |  __repr__(self, /)\n",
      "     |      Return repr(self).\n",
      "     |  \n",
      "     |  __setattr__(self, name, value, /)\n",
      "     |      Implement setattr(self, name, value).\n",
      "     |  \n",
      "     |  __setstate__(...)\n",
      "     |  \n",
      "     |  __str__(self, /)\n",
      "     |      Return str(self).\n",
      "     |  \n",
      "     |  with_traceback(...)\n",
      "     |      Exception.with_traceback(tb) --\n",
      "     |      set self.__traceback__ to tb and return self.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Data descriptors inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __cause__\n",
      "     |      exception cause\n",
      "     |  \n",
      "     |  __context__\n",
      "     |      exception context\n",
      "     |  \n",
      "     |  __dict__\n",
      "     |  \n",
      "     |  __suppress_context__\n",
      "     |  \n",
      "     |  __traceback__\n",
      "     |  \n",
      "     |  args\n",
      "    \n",
      "    class BufferTooShort(ProcessError)\n",
      "     |  Common base class for all non-exit exceptions.\n",
      "     |  \n",
      "     |  Method resolution order:\n",
      "     |      BufferTooShort\n",
      "     |      ProcessError\n",
      "     |      builtins.Exception\n",
      "     |      builtins.BaseException\n",
      "     |      builtins.object\n",
      "     |  \n",
      "     |  Data descriptors inherited from ProcessError:\n",
      "     |  \n",
      "     |  __weakref__\n",
      "     |      list of weak references to the object (if defined)\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __init__(self, /, *args, **kwargs)\n",
      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Static methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __new__(*args, **kwargs) from builtins.type\n",
      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __delattr__(self, name, /)\n",
      "     |      Implement delattr(self, name).\n",
      "     |  \n",
      "     |  __getattribute__(self, name, /)\n",
      "     |      Return getattr(self, name).\n",
      "     |  \n",
      "     |  __reduce__(...)\n",
      "     |      Helper for pickle.\n",
      "     |  \n",
      "     |  __repr__(self, /)\n",
      "     |      Return repr(self).\n",
      "     |  \n",
      "     |  __setattr__(self, name, value, /)\n",
      "     |      Implement setattr(self, name, value).\n",
      "     |  \n",
      "     |  __setstate__(...)\n",
      "     |  \n",
      "     |  __str__(self, /)\n",
      "     |      Return str(self).\n",
      "     |  \n",
      "     |  with_traceback(...)\n",
      "     |      Exception.with_traceback(tb) --\n",
      "     |      set self.__traceback__ to tb and return self.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Data descriptors inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __cause__\n",
      "     |      exception cause\n",
      "     |  \n",
      "     |  __context__\n",
      "     |      exception context\n",
      "     |  \n",
      "     |  __dict__\n",
      "     |  \n",
      "     |  __suppress_context__\n",
      "     |  \n",
      "     |  __traceback__\n",
      "     |  \n",
      "     |  args\n",
      "    \n",
      "    class Process(multiprocessing.process.BaseProcess)\n",
      "     |  Process(group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None)\n",
      "     |  \n",
      "     |  Process objects represent activity that is run in a separate process\n",
      "     |  \n",
      "     |  The class is analogous to `threading.Thread`\n",
      "     |  \n",
      "     |  Method resolution order:\n",
      "     |      Process\n",
      "     |      multiprocessing.process.BaseProcess\n",
      "     |      builtins.object\n",
      "     |  \n",
      "     |  Methods inherited from multiprocessing.process.BaseProcess:\n",
      "     |  \n",
      "     |  __init__(self, group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None)\n",
      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
      "     |  \n",
      "     |  __repr__(self)\n",
      "     |      Return repr(self).\n",
      "     |  \n",
      "     |  close(self)\n",
      "     |      Close the Process object.\n",
      "     |      \n",
      "     |      This method releases resources held by the Process object.  It is\n",
      "     |      an error to call this method if the child process is still running.\n",
      "     |  \n",
      "     |  is_alive(self)\n",
      "     |      Return whether process is alive\n",
      "     |  \n",
      "     |  join(self, timeout=None)\n",
      "     |      Wait until child process terminates\n",
      "     |  \n",
      "     |  kill(self)\n",
      "     |      Terminate process; sends SIGKILL signal or uses TerminateProcess()\n",
      "     |  \n",
      "     |  run(self)\n",
      "     |      Method to be run in sub-process; can be overridden in sub-class\n",
      "     |  \n",
      "     |  start(self)\n",
      "     |      Start child process\n",
      "     |  \n",
      "     |  terminate(self)\n",
      "     |      Terminate process; sends SIGTERM signal or uses TerminateProcess()\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Data descriptors inherited from multiprocessing.process.BaseProcess:\n",
      "     |  \n",
      "     |  __dict__\n",
      "     |      dictionary for instance variables (if defined)\n",
      "     |  \n",
      "     |  __weakref__\n",
      "     |      list of weak references to the object (if defined)\n",
      "     |  \n",
      "     |  authkey\n",
      "     |  \n",
      "     |  daemon\n",
      "     |      Return whether process is a daemon\n",
      "     |  \n",
      "     |  exitcode\n",
      "     |      Return exit code of process or `None` if it has yet to stop\n",
      "     |  \n",
      "     |  ident\n",
      "     |      Return identifier (PID) of process or `None` if it has yet to start\n",
      "     |  \n",
      "     |  name\n",
      "     |  \n",
      "     |  pid\n",
      "     |      Return identifier (PID) of process or `None` if it has yet to start\n",
      "     |  \n",
      "     |  sentinel\n",
      "     |      Return a file descriptor (Unix) or handle (Windows) suitable for\n",
      "     |      waiting for process termination.\n",
      "    \n",
      "    class ProcessError(builtins.Exception)\n",
      "     |  Common base class for all non-exit exceptions.\n",
      "     |  \n",
      "     |  Method resolution order:\n",
      "     |      ProcessError\n",
      "     |      builtins.Exception\n",
      "     |      builtins.BaseException\n",
      "     |      builtins.object\n",
      "     |  \n",
      "     |  Data descriptors defined here:\n",
      "     |  \n",
      "     |  __weakref__\n",
      "     |      list of weak references to the object (if defined)\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __init__(self, /, *args, **kwargs)\n",
      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Static methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __new__(*args, **kwargs) from builtins.type\n",
      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __delattr__(self, name, /)\n",
      "     |      Implement delattr(self, name).\n",
      "     |  \n",
      "     |  __getattribute__(self, name, /)\n",
      "     |      Return getattr(self, name).\n",
      "     |  \n",
      "     |  __reduce__(...)\n",
      "     |      Helper for pickle.\n",
      "     |  \n",
      "     |  __repr__(self, /)\n",
      "     |      Return repr(self).\n",
      "     |  \n",
      "     |  __setattr__(self, name, value, /)\n",
      "     |      Implement setattr(self, name, value).\n",
      "     |  \n",
      "     |  __setstate__(...)\n",
      "     |  \n",
      "     |  __str__(self, /)\n",
      "     |      Return str(self).\n",
      "     |  \n",
      "     |  with_traceback(...)\n",
      "     |      Exception.with_traceback(tb) --\n",
      "     |      set self.__traceback__ to tb and return self.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Data descriptors inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __cause__\n",
      "     |      exception cause\n",
      "     |  \n",
      "     |  __context__\n",
      "     |      exception context\n",
      "     |  \n",
      "     |  __dict__\n",
      "     |  \n",
      "     |  __suppress_context__\n",
      "     |  \n",
      "     |  __traceback__\n",
      "     |  \n",
      "     |  args\n",
      "    \n",
      "    class TimeoutError(ProcessError)\n",
      "     |  Common base class for all non-exit exceptions.\n",
      "     |  \n",
      "     |  Method resolution order:\n",
      "     |      TimeoutError\n",
      "     |      ProcessError\n",
      "     |      builtins.Exception\n",
      "     |      builtins.BaseException\n",
      "     |      builtins.object\n",
      "     |  \n",
      "     |  Data descriptors inherited from ProcessError:\n",
      "     |  \n",
      "     |  __weakref__\n",
      "     |      list of weak references to the object (if defined)\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __init__(self, /, *args, **kwargs)\n",
      "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Static methods inherited from builtins.Exception:\n",
      "     |  \n",
      "     |  __new__(*args, **kwargs) from builtins.type\n",
      "     |      Create and return a new object.  See help(type) for accurate signature.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Methods inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __delattr__(self, name, /)\n",
      "     |      Implement delattr(self, name).\n",
      "     |  \n",
      "     |  __getattribute__(self, name, /)\n",
      "     |      Return getattr(self, name).\n",
      "     |  \n",
      "     |  __reduce__(...)\n",
      "     |      Helper for pickle.\n",
      "     |  \n",
      "     |  __repr__(self, /)\n",
      "     |      Return repr(self).\n",
      "     |  \n",
      "     |  __setattr__(self, name, value, /)\n",
      "     |      Implement setattr(self, name, value).\n",
      "     |  \n",
      "     |  __setstate__(...)\n",
      "     |  \n",
      "     |  __str__(self, /)\n",
      "     |      Return str(self).\n",
      "     |  \n",
      "     |  with_traceback(...)\n",
      "     |      Exception.with_traceback(tb) --\n",
      "     |      set self.__traceback__ to tb and return self.\n",
      "     |  \n",
      "     |  ----------------------------------------------------------------------\n",
      "     |  Data descriptors inherited from builtins.BaseException:\n",
      "     |  \n",
      "     |  __cause__\n",
      "     |      exception cause\n",
      "     |  \n",
      "     |  __context__\n",
      "     |      exception context\n",
      "     |  \n",
      "     |  __dict__\n",
      "     |  \n",
      "     |  __suppress_context__\n",
      "     |  \n",
      "     |  __traceback__\n",
      "     |  \n",
      "     |  args\n",
      "\n",
      "FUNCTIONS\n",
      "    Array(typecode_or_type, size_or_initializer, *, lock=True) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a synchronized shared array\n",
      "    \n",
      "    Barrier(parties, action=None, timeout=None) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a barrier object\n",
      "    \n",
      "    BoundedSemaphore(value=1) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a bounded semaphore object\n",
      "    \n",
      "    Condition(lock=None) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a condition object\n",
      "    \n",
      "    Event() method of multiprocessing.context.DefaultContext instance\n",
      "        Returns an event object\n",
      "    \n",
      "    JoinableQueue(maxsize=0) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a queue object\n",
      "    \n",
      "    Lock() method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a non-recursive lock object\n",
      "    \n",
      "    Manager() method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a manager associated with a running server process\n",
      "        \n",
      "        The managers methods such as `Lock()`, `Condition()` and `Queue()`\n",
      "        can be used to create shared objects.\n",
      "    \n",
      "    Pipe(duplex=True) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns two connection object connected by a pipe\n",
      "    \n",
      "    Pool(processes=None, initializer=None, initargs=(), maxtasksperchild=None) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a process pool object\n",
      "    \n",
      "    Queue(maxsize=0) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a queue object\n",
      "    \n",
      "    RLock() method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a recursive lock object\n",
      "    \n",
      "    RawArray(typecode_or_type, size_or_initializer) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a shared array\n",
      "    \n",
      "    RawValue(typecode_or_type, *args) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a shared object\n",
      "    \n",
      "    Semaphore(value=1) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a semaphore object\n",
      "    \n",
      "    SimpleQueue() method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a queue object\n",
      "    \n",
      "    Value(typecode_or_type, *args, lock=True) method of multiprocessing.context.DefaultContext instance\n",
      "        Returns a synchronized shared object\n",
      "    \n",
      "    active_children()\n",
      "        Return list of process objects corresponding to live child processes\n",
      "    \n",
      "    allow_connection_pickling() method of multiprocessing.context.DefaultContext instance\n",
      "        Install support for sending connections and sockets\n",
      "        between processes\n",
      "    \n",
      "    cpu_count() method of multiprocessing.context.DefaultContext instance\n",
      "        Returns the number of CPUs in the system\n",
      "    \n",
      "    current_process()\n",
      "        Return process object representing the current process\n",
      "    \n",
      "    freeze_support() method of multiprocessing.context.DefaultContext instance\n",
      "        Check whether this is a fake forked process in a frozen executable.\n",
      "        If so then run code specified by commandline and exit.\n",
      "    \n",
      "    get_all_start_methods() method of multiprocessing.context.DefaultContext instance\n",
      "    \n",
      "    get_context(method=None) method of multiprocessing.context.DefaultContext instance\n",
      "    \n",
      "    get_logger() method of multiprocessing.context.DefaultContext instance\n",
      "        Return package logger -- if it does not already exist then\n",
      "        it is created.\n",
      "    \n",
      "    get_start_method(allow_none=False) method of multiprocessing.context.DefaultContext instance\n",
      "    \n",
      "    log_to_stderr(level=None) method of multiprocessing.context.DefaultContext instance\n",
      "        Turn on logging and add a handler which prints to stderr\n",
      "    \n",
      "    set_executable(executable) method of multiprocessing.context.DefaultContext instance\n",
      "        Sets the path to a python.exe or pythonw.exe binary used to run\n",
      "        child processes instead of sys.executable when using the 'spawn'\n",
      "        start method.  Useful for people embedding Python.\n",
      "    \n",
      "    set_forkserver_preload(module_names) method of multiprocessing.context.DefaultContext instance\n",
      "        Set list of module names to try to load in forkserver process.\n",
      "        This is really just a hint.\n",
      "    \n",
      "    set_start_method(method, force=False) method of multiprocessing.context.DefaultContext instance\n",
      "\n",
      "DATA\n",
      "    __all__ = ['Array', 'AuthenticationError', 'Barrier', 'BoundedSemaphor...\n",
      "\n",
      "FILE\n",
      "    /usr/lib/python3.7/multiprocessing/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#更多关于multiprocessing的介绍运行下面代码\n",
    "help(\"multiprocessing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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